Fetch real-time cryptocurrency prices and 24-hour market data for multiple coins. Returns current price in USD, 24h price change percentage, market cap, and trading volume. Use for crypto portfolio tracking, price alerts, or comparing digital assets.
| Name | Required | Description | Default |
|---|---|---|---|
| coins | No | List of cryptocurrency symbols or names to fetch price data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true, openWorldHint=true) already establish safety and openness. The description adds the return data details (price, change, market cap, volume), which is useful but not additional behavioral context like rate limits or data freshness. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main verb and scope. Every sentence earns its place: the first states the action and data scope, the second details outputs and use cases. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one optional param, no output schema), the description adequately conveys what it returns and why to use it. It lacks mention of what happens with no coins input, but overall it is complete enough for basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a detailed param description ('List of cryptocurrency symbols or names to fetch price data'). The tool description adds only the idea of 'multiple coins' without clarifying behavior when the optional parameter is omitted (required=0), so it adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function ('Fetch real-time cryptocurrency prices and 24-hour market data for multiple coins') and specifies the exact output fields (price, change, market cap, volume). This distinguishes it from sibling tools like get_exchange_rates (fiat) and get_stock_data (stocks).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear use cases ('crypto portfolio tracking, price alerts, or comparing digital assets') that signal appropriate contexts. However, it does not explicitly mention when not to use it or how it differs from the sibling track_crypto_portfolio, so it lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes: crypto prices, exchange rates, stock data, stock screening, and crypto portfolio tracking. However, get_crypto_prices and track_crypto_portfolio both return crypto price data, and get_stock_data and screen_stocks are related but distinct in action.
All tool names follow a consistent verb_noun pattern in snake_case (get_, screen_, track_). The 'get_' prefix is used for three tools, with 'screen_' and 'track_' as appropriate action verbs. No mixed conventions.
Five tools is well-scoped for a finance server covering cryptocurrencies, exchange rates, and stocks. Each tool earns its place, covering core data retrieval and screening functions without unnecessary bloat.
The tool set covers key finance use cases: price lookups, screening, and portfolio tracking. Minor gaps exist, such as no historical price data or stock portfolio tracking, but the primary domain is well-served.